Mercurial > repos > galaxyp > cardinal_segmentations

annotate segmentation.xml @ 2:4ba8fcfef86f draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit f127be2141cf22e269c85282d226eb16fe14a9c1
author galaxyp
date Fri, 15 Feb 2019 10:05:48 -0500
parents bb63d8ef0379
children ea5eb5ec208d
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1 <tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.2">
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2 <description>mass spectrometry imaging spatial clustering</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements">
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7 <requirement type="package" version="2.3">r-gridextra</requirement>
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8 <requirement type="package" version="0.20_35">r-lattice</requirement>
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9 </expand>
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10 <command detect_errors="exit_code">
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11 <![CDATA[
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13 @INPUT_LINKING@
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14 cat '${MSI_segmentation}' &&
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15 Rscript '${MSI_segmentation}'
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17 ]]>
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18 </command>
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19 <configfiles>
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20 <configfile name="MSI_segmentation"><![CDATA[
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23 ################################# load libraries and read file #################
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25 library(Cardinal)
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26 library(gridExtra)
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27 library(lattice)
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31 @READING_MSIDATA_INRAM@
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33 ## to make sure that processed files work as well:
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34 iData(msidata) = iData(msidata)[]
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35
2
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36 ## count and print number of NAs, all methods are not compatible with NAs
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37 print(paste0("Number of NA in dataset: ", sum(is.na(spectra(msidata)[])), " - segmentation does not work with NA values"))
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39 @DATA_PROPERTIES_INRAM@
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0
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42 ######################################## PDF ###################################
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43 ################################################################################
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44 ################################################################################
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47 pdf("segmentationpdf.pdf", fonts = "Times", pointsize = 12)
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48 plot(0,type='n',axes=FALSE,ann=FALSE)
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50 title(main=paste0("Spatial segmentation for file: \n\n", "$infile.display_name"))
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53 ############################# I) numbers ####################################
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54 #############################################################################
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55 grid.table(property_df, rows= NULL)
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56
2
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57 if (npeaks > 0 && sum(is.na(spectra(msidata)[]))==0)
0
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58 {
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60 ######################## II) segmentation tools #############################
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61 #############################################################################
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62 #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours])
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63 colourvector = c($color_string)
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64
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65 ### preparation for images and plots:
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66 #if str($image_cond.image_type) == "standard_image":
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67 print("standard image")
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68
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69 strip_input = TRUE
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70 lattice_input = FALSE
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72 #elif str($image_cond.image_type) == "lattice_image":
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73 print("lattice image")
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75 strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))
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76 lattice_input = TRUE
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78 #end if
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80 ## set seed to make analysis reproducible
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81 set.seed($setseed)
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83 #if str( $segm_cond.segmentationtool ) == 'pca':
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84 print('pca')
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85 ##pca
1
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86
0
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87 component_vector = character()
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88 for (numberofcomponents in 1:$segm_cond.pca_ncomp)
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89 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)}
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90
0
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91 pca_result = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE,
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92 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
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93
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94 ## remove msidata to clean up RAM space
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95 rm(msidata)
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96 gc()
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97
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98 ### table in pdf file
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99 plot(0,type='n',axes=FALSE,ann=FALSE)
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100 sd_table = as.data.frame(round(pca_result@resultData\$ncomp\$sdev, digits=2))
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101 colnames(sd_table) = "Standard deviation"
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102 PC_vector = character()
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103 for (PCs in 1:$segm_cond.pca_ncomp){
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104 PC_vector[[PCs]] = c(paste0("PC",PCs))}
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105 sd_table = cbind(PC_vector, sd_table)
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106 colnames(sd_table)[1] = "Principal components"
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107 grid.table(sd_table, rows=NULL)
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108 ### images in pdf file
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109 print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector, ylim=c(maximumy+2, minimumy-2)))
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110 for (PCs in 1:$segm_cond.pca_ncomp){
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111 print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
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112 ### plots in pdf file
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113 print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
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114 for (PCs in 1:$segm_cond.pca_ncomp){
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115 print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))}
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116
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117 ### values in tabular files
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118 pcaloadings = formatC(pca_result@resultData\$ncomp\$loadings, format = "e", digits = 6)### loading for each m/z value
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119 pcaloadings2 = cbind(matrix(unlist(strsplit(rownames(pcaloadings), " = ")), ncol=2, byrow=TRUE)[,2], pcaloadings)
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120 colnames(pcaloadings2) = c("mz", colnames(pcaloadings))
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121 pcascores = round(pca_result@resultData\$ncomp\$scores, digits=6) ### scores for each pixel
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122
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123 ## pixel names and coordinates
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124 ## to remove potential sample names and z dimension, split at comma and take only x and y
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125 x_coords = unlist(lapply(strsplit(rownames(pcascores), ","), `[[`, 1))
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126 y_coords = unlist(lapply(strsplit(rownames(pcascores), ","), `[[`, 2))
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127 x_coordinates = gsub("x = ","",x_coords)
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128 y_coordinates = gsub(" y = ","",y_coords)
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129
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130 pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates)
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131 pcascores2 = data.frame(pixel_names, x_coordinates, y_coordinates, pcascores)
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132 colnames(pcascores2) = c("pixel names", "x", "y", colnames(pcascores))
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133 write.table(pcaloadings2, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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134 write.table(pcascores2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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135
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136 ## optional output as .RData
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137 #if $output_rdata:
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138 ## save as (.RData)
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139 save(pca, file="$segmentation_rdata")
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140
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141 #end if
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142
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143 #elif str( $segm_cond.segmentationtool ) == 'kmeans':
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144 print('kmeans')
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145 ##k-means
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146
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147 skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method")
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148
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149 ## remove msidata to clean up RAM space
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150 rm(msidata)
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151 gc()
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152
0
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153 print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
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154
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155 print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c(1,1)))
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156
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157 skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
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158 for (iteration in 1:length(skm@resultData)){
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159 skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
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160 skm_clusters = cbind(skm_clusters, skm_cluster) }
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161
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162 ## pixel names and coordinates
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163 ## to remove potential sample names and z dimension, split at comma and take only x and y
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164 x_coords = unlist(lapply(strsplit(rownames(skm_clusters), ","), `[[`, 1))
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165 y_coords = unlist(lapply(strsplit(rownames(skm_clusters), ","), `[[`, 2))
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166 x_coordinates = gsub("x = ","",x_coords)
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167 y_coordinates = gsub(" y = ","",y_coords)
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168 pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates)
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169 skm_clusters2 = data.frame(pixel_names, x_coordinates, y_coordinates, skm_clusters)
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170 colnames(skm_clusters2) = c("pixel names", "x", "y",names(skm@resultData))
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171
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172 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
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173
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174 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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175 write.table(skm_clusters2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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176
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177 ## optional output as .RData
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178 #if $output_rdata:
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179
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180 ## save as (.RData)
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181 save(skm, file="$segmentation_rdata")
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182
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183 #end if
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184
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185 #elif str( $segm_cond.segmentationtool ) == 'centroids':
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186 print('centroids')
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187 ##centroids
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188
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189 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method")
2
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190 ## remove msidata to clean up RAM space
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191 rm(msidata)
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192 gc()
0
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193 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
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194 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c(1,1)))
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195 print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c(1,1), main="t-statistics", col=colourvector))
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196 plot(summary(ssc), main = "Number of segments")
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197
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198 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
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199 for (iteration in 1:length(ssc@resultData)){
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200 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
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201 ssc_classes = cbind(ssc_classes, ssc_class) }
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202
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203 ## pixel names and coordinates
1
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204 ## to remove potential sample names and z dimension, split at comma and take only x and y
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205 x_coords = unlist(lapply(strsplit(rownames(ssc_classes), ","), `[[`, 1))
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206 y_coords = unlist(lapply(strsplit(rownames(ssc_classes), ","), `[[`, 2))
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207 x_coordinates = gsub("x = ","",x_coords)
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208 y_coordinates = gsub(" y = ","",y_coords)
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209 pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates)
0
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210 ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
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211 colnames(ssc_classes2) = c("pixel names", "x", "y", names(ssc@resultData))
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212
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213 ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels)
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214
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215 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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216 write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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217
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218 ## optional output as .RData
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219 #if $output_rdata:
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220
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221 ## save as (.RData)
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222 save(ssc, file="$segmentation_rdata")
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223
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224 #end if
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225
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226 #end if
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227
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228 dev.off()
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229
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230 }else{
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231 print("Inputfile has no intensities > 0")
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232 dev.off()
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233 }
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234
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235 ]]></configfile>
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236 </configfiles>
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237 <inputs>
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238 <expand macro="reading_msidata"/>
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239 <conditional name="segm_cond">
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240 <param name="segmentationtool" type="select" label="Select the tool for spatial clustering">
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241 <option value="pca" selected="True">pca</option>
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242 <option value="kmeans">k-means</option>
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243 <option value="centroids">spatial shrunken centroids</option>
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244 </param>
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245 <when value="pca">
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246 <param name="pca_ncomp" type="integer" value="2"
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247 label="The number of principal components to calculate"/>
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248 <param name="pca_method" type="select"
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249 label="The function used to calculate the singular value decomposition">
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250 <option value="irlba" selected="True">irlba</option>
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251 <option value="svd">svd</option>
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252 </param>
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253 <param name="pca_scale" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Scaling of data before analysis"/>
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254 </when>
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255
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256 <when value="kmeans">
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257 <param name="kmeans_r" type="text" value="2"
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258 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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259 <expand macro="sanitizer_multiple_digits"/>
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260 </param>
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261 <param name="kmeans_k" type="text" value="3"
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262 label="The number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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263 <expand macro="sanitizer_multiple_digits"/>
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264 </param>
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265 <param name="kmeans_method" type="select" display="radio"
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266 label="The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering">
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267 <option value="gaussian">gaussian</option>
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268 <option value="adaptive" selected="True">adaptive</option>
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269 </param>
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270 <param name="kmeans_toplabels" type="integer" value="500"
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271 label="Number of toplabels (m/z) which should be written in tabular output"/>
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272 </when>
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273
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274 <when value="centroids">
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275 <param name="centroids_r" type="text" value="2"
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276 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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277 <expand macro="sanitizer_multiple_digits"/>
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278 </param>
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279 <param name="centroids_k" type="text" value="5"
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280 label="The initial number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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281 <expand macro="sanitizer_multiple_digits"/>
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282 </param>
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283 <param name="centroids_s" type="text" value="2"
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284 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)"
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285 help="As s increases, fewer m/z features (m/z values) will be used in the spatial segmentation, and only the informative m/z features will be retained. Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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286 <expand macro="sanitizer_multiple_digits"/>
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287 </param>
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288 <param name="centroids_method" type="select" display="radio" label="The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) weights, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) weights">
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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289 <option value="gaussian">gaussian</option>
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290 <option value="adaptive" selected="True">adaptive</option>
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291 </param>
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292 <param name="centroids_toplabels" type="integer" value="500"
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293 label="Number of toplabels (m/z) which should be written in tabular output"/>
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294 </when>
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295 </conditional>
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296 <conditional name="image_cond">
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297 <param name="image_type" type="select" label="Select the image type">
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298 <option value="standard_image" selected="True">standard</option>
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299 <option value="lattice_image">lattice</option>
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300 </param>
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301 <when value="standard_image"/>
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302 <when value="lattice_image"/>
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303 </conditional>
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304 <repeat name="colours" title="Colours for the plots" min="1" max="50">
2
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305 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of colours should be the same as number of components">
0
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306 <sanitizer>
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307 <valid initial="string.letters,string.digits">
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308 <add value="#" />
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309 </valid>
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310 </sanitizer>
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311 </param>
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312 </repeat>
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313 <param name="output_rdata" type="boolean" label="Results as .RData output"/>
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314 <param name="setseed" type="integer" value="1" label="set seed" help="Use same value to reproduce previous results"/>
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315 </inputs>
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316 <outputs>
2
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317 <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "${tool.name} on ${on_string}: results"/>
0
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318 <data format="tabular" name="mzfeatures" label="${tool.name} on ${on_string}: features"/>
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319 <data format="tabular" name="pixeloutput" label="${tool.name} on ${on_string}: pixels"/>
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320 <data format="rdata" name="segmentation_rdata" label="${tool.name} on ${on_string}: results.RData">
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321 <filter>output_rdata</filter>
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322 </data>
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323 </outputs>
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324 <tests>
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325 <test>
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326 <expand macro="infile_imzml"/>
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327 <param name="segmentationtool" value="pca"/>
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328 <param name="image_type" value="lattice_image"/>
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329 <repeat name="colours">
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330 <param name="feature_color" value="#ff00ff"/>
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331 </repeat>
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332 <repeat name="colours">
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333 <param name="feature_color" value="#0000FF"/>
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334 </repeat>
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335 <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size"/>
2
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336 <output name="mzfeatures">
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337 <assert_contents>
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338 <has_text text="300.17" />
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339 <has_text text="-4.234458e-04" />
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340 <has_text text="3.878545e-10" />
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341 <has_n_columns n="3" />
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342 </assert_contents>
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343 </output>
0
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344 <output name="pixeloutput" file="scores_pca.tabular"/>
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345 </test>
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346 <test>
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347 <expand macro="infile_analyze75"/>
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348 <param name="segmentationtool" value="kmeans"/>
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349 <param name="kmeans_r" value="1:3"/>
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350 <param name="kmeans_k" value="2,3"/>
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351 <param name="kmeans_toplabels" value="20"/>
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352 <repeat name="colours">
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353 <param name="feature_color" value="#ff00ff"/>
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354 </repeat>
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355 <repeat name="colours">
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356 <param name="feature_color" value="#0000FF"/>
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357 </repeat>
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358 <repeat name="colours">
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359 <param name="feature_color" value="#00C957"/>
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360 </repeat>
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361 <param name="output_rdata" value="True"/>
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362 <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size"/>
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363 <output name="mzfeatures" file="toplabels_skm.tabular"/>
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364 <output name="pixeloutput" file="cluster_skm.tabular"/>
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365 <output name="segmentation_rdata" file="cluster_skm.RData" compare="sim_size"/>
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366 </test>
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367 <test>
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368 <param name="infile" value="preprocessed.RData" ftype="rdata"/>
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369 <param name="segmentationtool" value="centroids"/>
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370 <param name="centroids_r" value="1,2"/>
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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371 <param name="centroids_k" value="3"/>
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372 <param name="centroids_toplabels" value="50"/>
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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373 <repeat name="colours">
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374 <param name="feature_color" value="#0000FF"/>
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375 </repeat>
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376 <repeat name="colours">
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377 <param name="feature_color" value="#00C957"/>
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378 </repeat>
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379 <repeat name="colours">
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380 <param name="feature_color" value="#B0171F"/>
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381 </repeat>
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382 <repeat name="colours">
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383 <param name="feature_color" value="#FFD700"/>
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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384 </repeat>
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385 <repeat name="colours">
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386 <param name="feature_color" value="#848484"/>
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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387 </repeat>
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388 <output name="segmentationimages" file="centroids_rdata.pdf" compare="sim_size"/>
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389 <output name="mzfeatures" file="toplabels_ssc.tabular"/>
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390 <output name="pixeloutput" file="classes_ssc.tabular"/>
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391 </test>
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392 </tests>
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393 <help>
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394 <![CDATA[
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395
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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396 @CARDINAL_DESCRIPTION@
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397
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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398 -----
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399
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400 This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data.
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401
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402 @MSIDATA_INPUT_DESCRIPTION@
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403
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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404 **Options**
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405
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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406 - PCA: principal component analysis
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407 - k-means: spatially-aware k-means clustering
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408 - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters
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409
be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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410 **Output**
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411
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412 - Pdf with the heatmaps and plots for the segmentation
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413 - Tabular file with information on m/z and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids)
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414 - Optional .RData file which contains the segmentation results and can be used for further exploration in R using the Cardinal package
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415
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416 ]]>
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417 </help>
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418 <expand macro="citations"/>
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419 </tool>